Triple
T10309589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hajdú-Bihar County |
E241851
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Polgár
Polgár is a town in eastern Hungary located in Hajdú-Bihar County, known for its agricultural surroundings and proximity to the Tisza River.
|
E997055
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Polgár | Statement: [Hajdú-Bihar County, containsCity, Polgár]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Polgár Context triple: [Hajdú-Bihar County, containsCity, Polgár]
-
A.
Kalocsa
Kalocsa is a historic town in southern Hungary known as an important Roman Catholic archiepiscopal center and for its traditional paprika production and folk art.
-
B.
Nagyvázsony
Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
-
C.
Nagykőrös
Nagykőrös is a historic town in central Hungary known for its agricultural traditions and small-town character.
-
D.
Oroszlány
Oroszlány is a town in northwestern Hungary known historically for its coal mining and industrial character.
-
E.
Harkány
Harkány is a Hungarian spa town in southern Transdanubia renowned for its medicinal thermal baths and health tourism.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Polgár Triple: [Hajdú-Bihar County, containsCity, Polgár]
Generated description
Polgár is a town in eastern Hungary located in Hajdú-Bihar County, known for its agricultural surroundings and proximity to the Tisza River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Polgár Target entity description: Polgár is a town in eastern Hungary located in Hajdú-Bihar County, known for its agricultural surroundings and proximity to the Tisza River.
-
A.
Kalocsa
Kalocsa is a historic town in southern Hungary known as an important Roman Catholic archiepiscopal center and for its traditional paprika production and folk art.
-
B.
Nagyvázsony
Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
-
C.
Nagykőrös
Nagykőrös is a historic town in central Hungary known for its agricultural traditions and small-town character.
-
D.
Oroszlány
Oroszlány is a town in northwestern Hungary known historically for its coal mining and industrial character.
-
E.
Harkány
Harkány is a Hungarian spa town in southern Transdanubia renowned for its medicinal thermal baths and health tourism.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d381ac38808190a8ca7457c85b625b |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d32a18ac81909b4efd8c1ba3e113 |
completed | April 7, 2026, 9:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f671788ec88190852df74698bc4518 |
completed | May 2, 2026, 9:49 p.m. |
| NEDg | Description generation | batch_69f67285019c8190be831d3f72cf121f |
completed | May 2, 2026, 9:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f67323a724819092425cdb3a070b96 |
completed | May 2, 2026, 9:56 p.m. |
Created at: April 6, 2026, 11:47 a.m.